Exercise 2 Data Import & Export - Solutions

Now we are in the second exercise session. Non-flat files were promised, so please load the GESIS Panel COVID-19 data.

This first part of the exercises only deals with importing data. Later, in the second exercise, we will turn more to non-flat data files and label them before exporting.

1

Load the GESIS Panel COVID-19 data.

Clues

You can either load the SPSS or Stata file for which you need the haven package.

solution

library(haven)

gp_covid <-
  read_spss("../data/ZA5667_v1-1-0.sav")

In contrast to the flat files, such as CSV, the variables now have labels. I wonder how the labels are for the first ten variables…

2

Print the labels of the first ten variables.

Clues

You can print labels with the function sjlabelled::get_label(your_data), but you have to make sure only to print the first ten variables.

solution

library(sjlabelled)

## 
## Attaching package: 'sjlabelled'

## The following objects are masked from 'package:haven':
## 
##     as_factor, read_sas, read_spss, read_stata, write_sas, zap_labels

get_label(gp_covid[1:10])

##                                za_number 
##              "Studiennummer des Archivs" 
##                                  version 
## "Versionskennung und -datum des Archivs" 
##                                      doi 
##        "Digital Object Identifier (doi)" 
##                                       id 
##                           "Befragten-ID" 
##                                   cohort 
##                   "Rekrutierungskohorte" 
##                                      sex 
##                             "Geschlecht" 
##                                  age_cat 
##                   "Alter, kategorisiert" 
##                            education_cat 
##                 "Bildung, kategorisiert" 
##                        intention_to_vote 
##               "Sonntagsfrage (gbzc011a)" 
##                          choice_of_party 
##         "Sonntagsfrage Wahlentscheidung"

Unfortunately, it’s all in German. Imagine you are an education researcher, and you are interested in the variable education_cat. So you may want to consider translating the variable into English, right?

3

Rename the variable label of age_cat from “Bildung, kategorisiert” into “Education, categorized”.

Clues

There a two ways: Either using the function sjlabelled::set_label() or do it in a pipe with sjlabelled::var_labels().

solution

# either
gp_covid$education_cat <- 
  set_label(
    gp_covid$education_cat, 
    label = "Education, categorized"
  )

# or
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:sjlabelled':
## 
##     as_label

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

gp_covid <- 
  gp_covid %>% 
  var_labels(
    education_cat = "Education, categorized"
  )

# proof
get_label(gp_covid$education_cat)

## [1] "Education, categorized"

Your colleague asks you to provide your new data after changing labels and stuff. Unfortunately, she does not use R or SPSS and asks you to export your data as a Stata file.

4

Export your data as a Stata file.

Clues

Like in the case of importing, you again need the haven package.

solution

write_stata(gp_covid, "gesis_panel_corona_fancy_panels_final_final.dta")

## Tidying value labels. Please wait...

## Writing stata file to 'gesis_panel_corona_fancy_panels_final_final.dta'. Please wait...